Transparent and Interpretable Conversational Agents

Smart-speakers including Amazon Alexa and Google Home are emerging
as a key consumer technology. Approximately 16% of adults (39 million)
in the United States now own one of these devices. As such, these
devices can shape the future of user interactions and engagements.
However, current devices have a range of usability issues, which
negatively impact users’ experiences and satisfactions.
Specifically, interactions with these devices often fail to: i)
support discoverability and learnability — users are unable to
explore and understand the capabilities of these devices, which
limits their usefulness; ii) provide adequate explanation regarding
what caused an error and how a user can rectify it — a key usability
issue which can lead to user frustrations and disengagement. These
issues stem from the lack of interpretability of underlying system,
which then can result in incorrect mental models for users.

In this project, we are aiming to address these issues by improving
the interpretability of smart-speakers and thus, leading to better
user mental models. Specifically, we will adopt a user-centric
approach to interpretability by determining both content (“what to
explain”) and presentation (“how to explain”) to users. While there
has been recent work on interpretability of complex systems,
smart-speakers have unique constraints and challenges (e.g., prior
work on using visualization for explaining decisions will not work
for voice-based interactions). Addressing these challenges can
improve quality of user interactions as well as extend the capabilities
of these systems to support complex user tasks.